Missing data is a major issue in many applied problems, especially in the biomedical
sciences. We review four common approaches for inference in generalized linear models …

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It sometimes happens (for instance in case control studies) that a classifier is trained on a
data set that does not reflect the true a priori probabilities of the target classes on real-world …

This book is concerned with statistical methods for the analysis of data collected from a
survey. A survey could consist of data collected from a questionnaire or from measurements …

Information from various public and private data sources of extremely large sample sizes are
now increasingly available for research purposes. Statistical methods are needed for using …

Multistate Models for the Analysis of Life History Data provides the first comprehensive
treatment of multistate modeling and analysis, including parametric, nonparametric and …

We consider semiparametric models for which solution of Horvitz–Thompson or inverse
probability weighted (IPW) likelihood equations with two‐phase stratified samples leads to …

In this paper we present an application of hybrid neural network approaches and an
assessment of the effects of missing data on motorway traffic flow forecasting. Two hybrid …

Missing data in clinical trials are inevitable. We highlight the ICH guidelines and CPMP
points to consider on missing data. Specifically, we outline how we should consider missing …

When I was a graduate student more than twenty five years ago, I was struggling to read
many statistical research papers. This is particularly true at the time when I had passed my …